Diversity recommendation based on multi-graph collaboration and graph convolution neural networks

Recommendation systems are the latest solution to solve the information surplus in the Internet era. However, the mainstream recommendation models have the problem of data sparsity and lack of diversity. Therefore, this paper proposes a diversity recommendation algorithm based on multi-graph collaboration and graph convolution neural networks (GCN) to solve the above problems. Specifically, we use the user-item bipartite graph to construct a multi-graph to mine the edge information of potential space, so as to improve the data sparsity problem and construct a diverse node neighbourhood, and we use the graph convolution neural networks to model the implicit characteristics of users and items, so as to learn users' diversity preferences. We conducted a series of experiments on real data sets, and the experimental results show the validity of our method (DR-MGCN).